Ensuring highly optimized image acquisitions is one of the key factors for accurate clinical diagnosis in healthcare. However, medical scanning depends on many input parameters such as image information (e.g., quality requirements), patient information (e.g., target organ), clinical protocol (e.g., scan duration), contrast medium utilized, and various scanner parameters. Collectively these parameters represent a complex parameter space that is often difficult to navigate in order to determine an optimal set of input parameters. As a result, parameter selection can be a time intensive process as an operator must explore different parameter combinations in order to achieve desired results. Moreover, many imaging tasks require a set of parameters that are individualized for the study being performed. As a result, even when an optimal set of parameters can be learned for one study, those parameters cannot be easily reused for other, dissimilar studies. Accordingly, it is desired to provide techniques for automating parameter value selection that can be utilized across a large number of imaging applications with minimal input from the operator.